資產配置為每個人一生中都會遇到的課題，而在投資過程中投資人視其個別的情況和投資目標，把資產投資到不同種類的投資標的上，例如：股票、債券、房地產及現金等，並在報酬與風險之間做取捨。
在一般的投資組合理論中，經常使用歷史報酬資料的期望值和變異數分別當作未來的報酬與風險，然而投資人傾向只把實際報酬低於期望報酬的部分當作風險，因此半變異數比起變異數，將是更適合的風險測量方法。而在現實金融市場中，投資人獲得的資訊往往是不完整的，且容易被含糊的語意形容詞影響，更存在著許多非機率因子，例如：政治因素、投資人心理因素及天災人禍等，種種因素導致未來報酬無法被精準的預測，而隨著模糊理論的提出，此類充滿著不確定性的問題，便可透過模糊數表達，並進一步求解。
過往的模糊多週期投資組合(fuzzy multi-period portfolio selection)文獻中報酬目標式的設定將導致一些問題，其中包括忽略了新資料的加入、投資人必須預設週期間隔時間導致無法因應市場變化以及因為每個週期的資產皆不同，將導致交易成本項中每個週期的投資比例標準不同，無法比較的情況。因此，本研究提出調整型模型，成功改善過去問題中報酬設定的缺陷，並加入定存融資與多項現實投資條件，再以模糊目標規劃代替單純最佳化的方式求解，利用歸屬程度表達對目標達成之滿意度，滿足不同投資人對於報酬與風險的要求。
結果顯示加入定存與融資設定除了可使投資組合解更優，也能擴大可行解範圍，且以下半變異數衡量風險可有效降低下行風險，滿足投資人對於實際報酬低於期望報酬的部分愈小愈好的偏好。

In modern portfolio theory, expected values and variances in the historical return data are used to measure future returns and risks, respectively. However, investors tend to take the part of the actual value that is below the expected value as the risk measurement. Therefore, instead of variance, semi-variance would be a more proper measurement of risk. The information that investors get in the real-world financial market is often incomplete, and there are also many non-probabilistic factors in real-world portfolio decision-making, including political and psychological factors, as well as natural or man-made disasters, that will affect the precision of predictions of future returns. Therefore, it is imperative to consider returns as fuzzy numbers and further to solve the uncertainty problem. In the past literature, researchers attempting to solve the fuzzy multi-period portfolio selection problem encountered problems, including ignoring new data and investors having to preset the interval time, which led to a lack of a response to changeable markets. Also, the capital in each period was different, so the proportion of capital invested in each period could not be compared. Therefore, in this study, an adjustment model was proposed, where the problems encountered in the past literature were successfully resolved, and where lending, borrowing, and real world investing constraints were added. In addition, the problem was solved using fuzzy goal programming instead of optimization, which used the fuzzy membership function to express the degree to which goals and requirements for returns and reduced risks for investors were met. The results showed that settings related to lending and borrowing not only create a better efficient frontier, but also expand the feasible solution region.

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